COT: Contextual Operating Tensor for Context-aware Recommender
Systems
for Large-Scale Categorical Data
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| Overview of our proposed COT model |
People
Qiang Liu
Shu Wu
Liang Wang
Overview
With rapid growth of information on the internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot capture the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this work, we propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector. Then, to model the semantic operation of a context combination, we generate contextual operating matrix from the contextual operating tensor and latent vectors of contexts. Thus latent vectors of users and items can be operated by the contextual operating matrices. Experimental results show that the proposed COT model yields significant improvements over the competitive compared methods on three typical datasets, i.e., Food, Adom and Movielens-1M datasets.
Paper
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Experimental Results
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| Performance comparison on three datasets and two kinds of splitting, measured by RMSE and MAE |
Acknowledgments
This work is jointly supported by National Basic Research Program of China (2012CB316300), National Natural Science Foundation of China (61403390, 61175003, 61135002) and Hundred Talents Program of CAS.